import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Tuple				  =		argparse.ArgumentParser(
      description=(
          """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
          """ Distillation"""
      )
  )
  parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
  parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
  parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
  parser.add_argument("""--vocab_transform""", action="""store_true""")
  SCREAMING_SNAKE_CASE			:						str				  =		parser.parse_args()

  if args.model_type == "bert":
    SCREAMING_SNAKE_CASE			:						Optional[int]				  =		BertForMaskedLM.from_pretrained(args.model_name)
    SCREAMING_SNAKE_CASE			:						Optional[int]				  =		'bert'
  else:
    raise ValueError("""args.model_type should be \"bert\".""")

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		model.state_dict()
  SCREAMING_SNAKE_CASE			:						Dict				  =		{}

  for w in ["word_embeddings", "position_embeddings"]:
    SCREAMING_SNAKE_CASE			:						List[str]				  =		state_dict[f'''{prefix}.embeddings.{w}.weight''']
  for w in ["weight", "bias"]:
    SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

  SCREAMING_SNAKE_CASE			:						List[Any]				  =		0
  for teacher_idx in [0, 2, 4, 7, 9, 11]:
    for w in ["weight", "bias"]:
      SCREAMING_SNAKE_CASE			:						str				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
      ]
      SCREAMING_SNAKE_CASE			:						List[Any]				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
      ]
      SCREAMING_SNAKE_CASE			:						str				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
      ]

      SCREAMING_SNAKE_CASE			:						Optional[int]				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE			:						Any				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
      ]

      SCREAMING_SNAKE_CASE			:						Dict				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE			:						List[Any]				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE			:						Any				  =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
      ]
    std_idx += 1

  SCREAMING_SNAKE_CASE			:						Tuple				  =		state_dict['cls.predictions.decoder.weight']
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		state_dict['cls.predictions.bias']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      SCREAMING_SNAKE_CASE			:						Optional[int]				  =		state_dict[f'''cls.predictions.transform.dense.{w}''']
      SCREAMING_SNAKE_CASE			:						str				  =		state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

  print(f'''N layers selected for distillation: {std_idx}''')
  print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

  print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
  torch.save(compressed_sd, args.dump_checkpoint)







'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase_  :							List[str]             =    ['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase_  :							int             =    'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():
							class 						lowerCamelCase_				(       tf.keras.Model      ):







													def __init__(	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					):

																				"""simple docstring"""
																				super().__init__()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer
																				SCREAMING_SNAKE_CASE       :   str         =      AutoConfig.from_pretrained(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TFAutoModel.from_config(lowerCAmelCase__					)







													def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Tuple					):

																				"""simple docstring"""
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.tokenizer(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      self.bert(**lowerCAmelCase__					)
																				return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().setUp()

													SCREAMING_SNAKE_CASE       :   int         =      [
													    BertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
													]  # repeat for when fast_bert_tokenizer=false
													SCREAMING_SNAKE_CASE       :   Dict         =      [TFBertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in TOKENIZER_CHECKPOINTS] + [
													    TFBertTokenizer.from_pretrained(lowerCAmelCase__					,     use_fast_bert_tokenizer=lowerCAmelCase__					)
													    for checkpoint in TOKENIZER_CHECKPOINTS
													]
													assert len(self.tokenizers					) == len(self.tf_tokenizers					)

													SCREAMING_SNAKE_CASE       :   Any         =      [
													    '''This is a straightforward English test sentence.''',
													    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
													    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
													    '''And some much more rare Chinese: 齉 堃 齉堃''',
													    '''Je vais aussi écrire en français pour tester les accents''',
													    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
													]
													SCREAMING_SNAKE_CASE       :   int         =      list(zip(self.test_sentences					,     self.test_sentences[::-1]					)					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													for tokenizer, tf_tokenizer in zip(self.tokenizers					,     self.tf_tokenizers					):
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer(lowerCAmelCase__					,     return_tensors='''tf'''					,     padding='''longest'''					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in python_outputs.keys():
																																		self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape					)					)
																																		self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,     tf.intaa					) == tf_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(self.paired_sentences					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(
																				    text=[sentence[0] for sentence in self.paired_sentences]					,     text_pair=[sentence[1] for sentence in self.paired_sentences]					,     )
																				for key in merged_outputs.keys():
																											self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,     tf.intaa					) == separated_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Any         =      tf.function(lowerCAmelCase__					)
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   int         =      tf.constant(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      compiled_tokenizer(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[str]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in eager_outputs.keys():
																																		self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   int         =      ModelToSave(tokenizer=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      tf.convert_to_tensor(self.test_sentences					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					)  # Build model with some sample inputs
																				with TemporaryDirectory() as tempdir:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      Path(lowerCAmelCase__					) / '''saved.model'''
																											model.save(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf.keras.models.load_model(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      loaded_model(lowerCAmelCase__					)
																				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output					)					)					,     1e-5					)




